Cardiff University | Prifysgol Caerdydd ORCA
Online Research @ Cardiff 
WelshClear Cookie - decide language by browser settings

Automatic semantic style transfer using deep convolutional neural networks and soft masks

Zhao, Hui-Huang, Rosin, Paul L. ORCID:, Lai, YuKun ORCID: and Wang, Yao-Nan 2020. Automatic semantic style transfer using deep convolutional neural networks and soft masks. Visual Computer 36 , pp. 1307-1324. 10.1007/s00371-019-01726-2

[thumbnail of style-mask-TVC-postprint.pdf]
PDF - Accepted Post-Print Version
Download (6MB) | Preview


This paper presents an automatic image synthesis method to transfer the style of an example image to a content image. When standard neural style transfer approaches are used, the textures and colours in different semantic regions of the style image are often applied inappropriately to the content image, ignoring its semantic layout and ruining the transfer result. In order to reduce or avoid such effects, we propose a novel method based on automatically segmenting the objects and extracting their soft semantic masks from the style and content images, in order to preserve the structure of the content image while having the style transferred. Each soft mask of the style image represents a specific part of the style image, corresponding to the soft mask of the content image with the same semantics. Both the soft masks and source images are provided as multichannel input to an augmented deep CNN framework for style transfer which incorporates a generative Markov random field model. The results on various images show that our method outperforms the most recent techniques.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Springer Verlag
ISSN: 0178-2789
Date of First Compliant Deposit: 10 July 2019
Date of Acceptance: 9 July 2019
Last Modified: 07 Nov 2023 08:12

Citation Data

Cited 23 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item Edit Item


Downloads per month over past year

View more statistics